Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	109,371
2	South Dakota	97,777
3	Iowa	77,964
4	Wisconsin	76,156
5	Nebraska	73,037
6	Utah	67,885
7	Montana	64,233
8	Wyoming	63,903
9	Minnesota	63,162
10	Illinois	62,957
11	Idaho	62,914
12	Rhode Island	62,129
13	Kansas	60,889
14	Tennessee	58,473
15	Indiana	57,937
16	Arkansas	57,009
17	Mississippi	55,841
18	Missouri	55,763
19	Nevada	55,568
20	Alabama	55,520
21	Oklahoma	55,190
22	Louisiana	54,236
23	New Mexico	52,434
24	Alaska	51,056
25	Arizona	50,333
26	Florida	49,622
27	Texas	46,841
28	Kentucky	46,342
29	Colorado	46,279
30	Georgia	45,983
31	South Carolina	45,574
32	Michigan	43,625
33	New Jersey	42,040
34	Delaware	41,807
35	Ohio	41,431
36	North Carolina	38,119
37	Connecticut	38,101
38	Massachusetts	37,624
39	New York	36,904
40	Maryland	36,000
41	California	35,410
42	Pennsylvania	33,776
43	District of Columbia	33,041
44	West Virginia	31,318
45	Virginia	30,328
46	Puerto Rico	28,567
47	Washington	25,143
48	Oregon	20,343
49	New Hampshire	18,986
50	Hawaii	13,178
51	Maine	10,247
52	Vermont	8,141

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,158
2	Minnesota	1,015
3	Alaska	1,007
4	Indiana	988
5	Nevada	875
6	Utah	871
7	Nebraska	850
8	Oklahoma	801
9	New Mexico	796
10	South Dakota	792
11	Delaware	791
12	Colorado	784
13	Ohio	779
14	Connecticut	760
15	Tennessee	756
16	West Virginia	735
17	Pennsylvania	721
18	Wyoming	718
19	Idaho	688
20	Illinois	686
21	California	683
22	Kansas	672
23	Montana	672
24	Wisconsin	641
25	Missouri	640
26	Kentucky	636
27	Massachusetts	625
28	North Dakota	609
29	New Jersey	559
30	South Carolina	559
31	Alabama	545
32	Arkansas	541
33	Michigan	533
34	Mississippi	523
35	New Hampshire	521
36	Arizona	517
37	North Carolina	496
38	New York	476
39	Iowa	470
40	Maryland	448
41	Virginia	448
42	Florida	412
43	Texas	408
44	District of Columbia	396
45	Washington	381
46	Georgia	378
47	Louisiana	355
48	Oregon	344
49	Puerto Rico	315
50	Maine	230
51	Vermont	169
52	Hawaii	67

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,951
2	New York	1,780
3	Massachusetts	1,601
4	Connecticut	1,465
5	Louisiana	1,421
6	Rhode Island	1,366
7	North Dakota	1,348
8	Mississippi	1,330
9	South Dakota	1,254
10	Illinois	1,123
11	Michigan	1,042
12	District of Columbia	993
13	Arizona	954
14	Indiana	933
15	Georgia	900
16	Arkansas	898
17	Florida	897
18	Pennsylvania	892
19	South Carolina	889
20	Iowa	863
21	New Mexico	837
22	Delaware	814
23	Maryland	804
24	Texas	802
25	Alabama	793
26	Nevada	755
27	Missouri	725
28	Tennessee	725
29	Minnesota	720
30	Montana	695
31	Wisconsin	683
32	Nebraska	643
33	Kansas	637
34	Ohio	600
35	Colorado	599
36	Idaho	594
37	North Carolina	533
38	Kentucky	509
39	California	507
40	Virginia	492
41	Wyoming	483
42	Oklahoma	482
43	West Virginia	469
44	New Hampshire	416
45	Washington	404
46	Puerto Rico	376
47	Utah	296
48	Oregon	248
49	Alaska	190
50	Hawaii	183
51	Maine	168
52	Vermont	129

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	South Dakota	17
2	Arkansas	14
3	North Dakota	14
4	Wyoming	13
5	Illinois	11
6	Rhode Island	11
7	Michigan	10
8	Minnesota	9
9	Indiana	8
10	Kansas	8
11	Connecticut	7
12	Nebraska	7
13	New Mexico	7
14	West Virginia	7
15	Wisconsin	7
16	Iowa	6
17	Massachusetts	6
18	Pennsylvania	6
19	Tennessee	6
20	Mississippi	5
21	Nevada	5
22	South Carolina	5
23	Alabama	4
24	Colorado	4
25	Florida	4
26	Idaho	4
27	Kentucky	4
28	Louisiana	4
29	Maryland	4
30	Missouri	4
31	Montana	4
32	Ohio	4
33	Oklahoma	4
34	Texas	4
35	Delaware	3
36	District of Columbia	3
37	Georgia	3
38	New Hampshire	3
39	New Jersey	3
40	New York	3
41	Oregon	3
42	Puerto Rico	3
43	Arizona	2
44	California	2
45	North Carolina	2
46	Utah	2
47	Vermont	2
48	Alaska	1
49	Hawaii	1
50	Virginia	1
51	Maine	0
52	Washington	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	222,241	1	99
Norton	Kansas	209,849	2	99
Bon Homme	South Dakota	203,449	3	99
Buffalo	South Dakota	199,286	4	99
Lincoln	Arkansas	197,635	5	99
Davidson	Tennessee	72,427	515	83
Richland	South Carolina	52,069	1346	57
York	South Carolina	39,160	2050	34
Orange	California	28,699	2533	19
Pierce	Washington	21,950	2771	11

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	6,458	2	99
Dickey	North Dakota	5,952	3	99
Foster	North Dakota	5,607	4	99
Gregory	South Dakota	5,496	5	99
Richland	South Carolina	743	1417	54
Davidson	Tennessee	634	1667	46
York	South Carolina	527	1895	39
Orange	California	514	1928	38
Pierce	Washington	370	2290	27

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.